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Meta-Analysis
. 2020 Aug;48(8):300060520941634.
doi: 10.1177/0300060520941634.

MicroRNA-122 as a diagnostic biomarker for hepatocellular carcinoma related to hepatitis C virus: a meta-analysis and systematic review

Xiao-Yu Wei  1 , Jing Ding  1 , Wen-Guang Tian  1 , Yi-Chuan Yu  2
Affiliations
Meta-Analysis

MicroRNA-122 as a diagnostic biomarker for hepatocellular carcinoma related to hepatitis C virus: a meta-analysis and systematic review

Xiao-Yu Wei et al. J Int Med Res. 2020 Aug.

Abstract

Objective: MicroRNA-122 (miR-122) has been identified as a biomarker of liver diseases. However, the miR-122 detection accuracy in patients with hepatocellular carcinoma (HCC) associated with hepatitis C virus (HCV) is inconclusive.

Methods: We conducted a systematic literature search of Web of Science, Cochrane Library, PubMed, and Embase to identify studies related to the diagnostic value of miR-122 in HCV-related HCC. We analyzed the results and validated them using data from the Cancer Genome Atlas (TCGA).

Results: Six articles were included in this meta-analysis, comprising 354 cases and 420 controls. The pooled specificity, sensitivity, positive likelihood ratio, negative likelihood ratio, diagnostic odds ratio, and area under the curve were 0.87, 0.83, 5.1, 0.16, 32, and 0.92, respectively. Additional sub-group analyses showed that results for plasma were more sensitive than those for serum. In addition, miR-122 was better at distinguishing between HCV-associated HCC and healthy people or those with HCV than between those with HCV-associated HCC and HCV-related cirrhosis. Small samples (≤100) had better diagnostic odds ratios than larger samples (>100). Analysis of data from TCGA confirmed that miRNA-122 had a high diagnostic value.

Conclusion: This meta-analysis demonstrates that miR-122 may be a useful diagnostic biomarker for HCV-associated HCC.

Keywords: diagnosis; hepatitis C virus; hepatocellular carcinoma; meta-analysis; microRNA-122; plasma.

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Figures

Figure 1.
Figure 1.
Flowchart of selection of studies for inclusion in the meta-analysis.
Figure 2.
Figure 2.
Quality of included studies and summary of bias risk. (a) Quality of included studies according to QUADAS-2 guidelines. (b) Summary of bias risk assessment results for QUADAS-2.
Figure 3.
Figure 3.
Forest plots of summary sensitivities and specificity of circulating miR-122 for the diagnosis of hepatitis C virus-associated hepatocellular carcinoma. df, degrees of freedom.
Figure 4.
Figure 4.
Summary receiver operating characteristic curves for miR-122 in the diagnosis of hepatitis C virus-associated hepatocellular carcinoma. SROC, summary receiver operating characteristic curve; AUC, area under the curve.
Figure 5.
Figure 5.
Sensitivity analysis. (a) Goodness-of-fit, (b) bivariate normality analysis, (c) influence, and (d) outlier detection analysis.
Figure 6.
Figure 6.
Deeks’ funnel plot asymmetry test for the assessment of potential publication bias.
Figure 7.
Figure 7.
Receiver operating characteristic curve for data from The Cancer Genome Atlas. (a) MiR-122-5p; (b) miR-122-3p. AUC, area under the curve.
See this image and copyright information in PMC

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